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Volume 17, Issue 34 (12-2021)                   marine-engineering 2021, 17(34): 1-11 | Back to browse issues page

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Karimi N, Bahreinimotlagh M, Farokhnia A, Roozbahani R, Bani Hashemi S M. Extraction of Caspian Sea coastline bathymetry map using satellite data. marine-engineering. 2021; 17 (34) :1-11
URL: http://marine-eng.ir/article-1-884-en.html
1- Water Research Institute
2- mbanihashemi@hotmail.com
Abstract:   (701 Views)
The main goal of the present study is to use satellite data to extract bathymetry maps of coastlines and especially the shores of the Caspian Sea. For this purpose, the area between the Neka power plant and Amirabad port in Mazandaran province was selected as a pilot. Landsat-OLI satellite image was used to extract the bathymetry map of the study area. Simultaneously with the path of the satellite, about 2700 points from the depths of 2 to 11 meters of the Caspian Sea, was measurement, of which 500 points were used as control points and the rest as training samples. The polynomial linear regression method was used to extract the bathymetry map. Also, a stepwise regression method was used to identify the best regression model and select the best independent variables to estimate the depth. Comparison between the water depth map extracted from the Landsat-OLI satellite image with the control points showed that the RMSE value of this sensor in estimating the coastal water depth was about 0.4 m with an average standard error of 7.6%. By considering the turbidity and roughness of the seawater of Caspian Sea, the obtained result is an acceptable accuracy.
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Type of Study: Research Paper | Subject: Environmental Study
Received: 2021/01/24 | Accepted: 2021/07/11

References
1. Ashphaq, M., Srivastava, P.K. and Mitra, D., (2021). Review of near-shore satellite derived bathymetry: classification and account of five decades of coastal bathymetry research, Journal of Ocean Engineering and Science, In press. [DOI:10.1016/j.joes.2021.02.006]
2. Karimi, N., Bagheri, M.H., Hooshyaripor, F., Farokhnia, A. and Sheshangosht, S., (2016), Deriving and Evaluating Bathymetry Maps and Stage Curves for Shallow Lakes Using Remote Sensing Data, Water Resources Management, Vol.30, p.5003-5020 [DOI:10.1007/s11269-016-1465-9]
3. Ceyhun, Ö. and Yalçın, A., (2010), Remote sensing of water depths in shallow waters via artificial neural networks, Estuarine, Coastal and Shelf Science, Vol.89, p.89-96 [DOI:10.1016/j.ecss.2010.05.015]
4. Yuzugullu, O. and Aksoy, A., (2013), Generation of the bathymetry of a eutrophic shallow lake using WorldView-2 imagery, Journal of Hydroinformatics, Vol.16, p.50-59 16 [DOI:10.2166/hydro.2013.133]
5. Makboul, O., Negm, A., Mesbah, S. and Mohasseb, M., (2017), Performance Assessment of ANN in Estimating Remotely Sensed Extracted Bathymetry. Case Study: Eastern Harbor of Alexandria, Procedia Engineering, Vol.181, p.912-919 [DOI:10.1016/j.proeng.2017.02.486]
6. Jawak, S. and Luis, A., (2016), High-resolution multispectral satellite imagery for extracting bathymetric information of Antarctic shallow lakes, Remote Sensing of the Oceans and Inland Waters: Techniques, Applications, and Challenges, Vol.9978, p.1-9 [DOI:10.1117/12.2222769]
7. Gholamalifard, M., Esmaili-Sari, A., Abkar, A. and Naimi, B., (2013), Bathymetric Modeling from Satellite Imagery via Single Band Algorithm (SBA) and Principal Components Analysis (PCA) in Southern Caspian Sea, International Journal of Environmental Research, Vol.7, p.877-886
8. Heidarian, K., Kaboodvandpour, Sh. And Amanollahi, G., (2016), Investigation of Zarivar international wetland depth changes using remote sensing and artificial neural network model, Journal of Geographic space, Vol.53, p.271-289 (In Persian)
9. Green, E.P., Edwards, A.J. and Mumby, P.J., (2000), Mapping Bathymetry. In Remote Sensing Handbook for Tropical Coastal Management, ed. A. Edwards, UNESCO, Paris, France, V.15, p.219 - 235
10. Arsen, A., Crétaux, J.-F., Berge-Nguyen, M. and Del Rio, R.A., (2014), Remote Sensing-Derived Bathymetry of Lake Poopó, Remote Sensing, Vol.6, p.407-420 [DOI:10.3390/rs6010407]
11. Ayana, E.K., Philpot, W.D., Melesse, A.M. and Steenhuis, T.S., (2014), Bathymetry, Lake Area and Volume Mapping: A Remote-Sensing Perspective. In A.M. Melesse, W. Abtew, & S.G. Setegn (Eds.), Nile River Basin (pp. 253-267): Springer International Publishing [DOI:10.1007/978-3-319-02720-3_14]
12. Duguay, C.R. and Lafleur, P.M, (2003), Determining depth and ice thickness of shallow sub-Arctic lakes using space-borne optical and SAR data, International Journal of Remote Sensing, Vol.24, p.475-489 [DOI:10.1080/01431160304992]
13. Jay, S. and Guillaume, M., (2014), A novel maximum likelihood based method for mapping depth and water quality from hyperspectral remote-sensing data, Remote Sensing of Environment, Vol.147, p.121-132 [DOI:10.1016/j.rse.2014.01.026]
14. Kanno, A., Tanaka, Y., Kurosawa, A. and Sekine, M, (2013), Generalized Lyzenga's Predictor of Shallow Water Depth for Multispectral Satellite Imagery, Marine Geodesy, Vol.36, p.365-376 [DOI:10.1080/01490419.2013.839974]
15. Majozi, N.P., Salama, M.S., Bernard, S., Harper, D.M. and Habte, M.G., (2014), Remote sensing of euphotic depth in shallow tropical inland waters of Lake Naivasha using MERIS data, Remote Sensing of Environment, Vol.148, p.178-189 [DOI:10.1016/j.rse.2014.03.025]
16. Tripathi, N.K. and Rao, A.M., (2002), Bathymetric mapping in Kakinada Bay, India, using IRS-1D LISS-III data, International Journal of Remote Sensing, Vol.23, p.1013-1025 [DOI:10.1080/01431160110075785]
17. Van Hengel, W. and Spitzer, D., (1991), Multi-temporal Water Depth Mapping by Means of Landsat TM, International Journal of Remote Sensing, Vol.4, p.703-712 [DOI:10.1080/01431169108929687]

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